Neuroprotection against superoxide anion radical by metallocorroles in cellular and murine models of optic neuropathy
Bibliographic record
Abstract
Corroles are tetrapyrrolic macrocycles that have come under increased attention because of their unique capabilities for oxidation catalysis, reduction catalysis, and biomedical applications. Corrole-metal complexes (metallocorroles) can decompose certain reactive oxygen species (ROS), similar to metalloporphyrins. We investigated whether Fe-, Mn-, and Ga-corroles have neuroprotective effects on neurons and correlated this with superoxide scavenging activity in vitro and in vivo. Apoptosis was induced in retinal ganglion cell-5 neuronal precursor cells by serum deprivation. Cell death was measured with sodium 3'-[1-[(phenylamino)-carbonyl]-3,4-tetrazolium]-bis (4-methoxy-6-nitro) benzene-sulfonic acid hydrate and calcein-AM/propidium iodide assays. Fe- and Mn-corroles, but not the non-redox-active Ga-corrole used as control, reduced RGC-5 cell death after serum deprivation. Serum deprivation caused increased levels of intracellular superoxide, detected by an increase in the fluorescence intensity of 2-hydroxyethidium, and this was blocked by Fe- and Mn-corroles, but not Ga-corrole. In vivo real-time confocal imaging of retinas after optic nerve transection assessed the superoxide production within individual rat retinal ganglion cells. Fe- and Mn-corroles, but not Ga-corrole, scavenged neuronal superoxide in vivo. Given that the neuroprotective activity of metallocorroles correlated with superoxide scavenging activity, Fe- and Mn-corroles could be candidate drugs for delaying neuronal death after axonal injury in optic neuropathies, such as glaucoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".